There are other related works, which aim to find suitable representations of raw data such that the performance in downstream prediction tasks is better when they use the learned embeddings. Large Margin Nearest Neighbour (LMNN) [1]} introduces the much-celebrated triplet loss. In this work, the authors find a data-specific Mahalanobis distance embedding for the data, subject to the constraint that \(k\)  similarly labeled points are mapped in the neighborhood of every point. The transformed data is more amenable for a \(k\)  Nearest Neighbour classifier to work with. [2]} extends LMNN to make it parameter-free.
Recently, there has been a surge of interest in contrastive learning [3]}, [4]} as well where the core idea is to group similar/dissimilar representations of data into similar/dissimilar groups. The latent representations thus generated are beneficial to supervised classifiers in classification tasks.
